Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real wo...

Descripción completa

Detalles Bibliográficos
Autores principales: Alonazi, Mohammed, Alshahrani, Haya Mesfer, Kouki, Fadoua, Almalki, Nabil Sharaf, Mahmud, Ahmed, Majdoubi, Jihen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669472/
https://www.ncbi.nlm.nih.gov/pubmed/37999195
http://dx.doi.org/10.3390/biomimetics8070554
_version_ 1785149234406752256
author Alonazi, Mohammed
Alshahrani, Haya Mesfer
Kouki, Fadoua
Almalki, Nabil Sharaf
Mahmud, Ahmed
Majdoubi, Jihen
author_facet Alonazi, Mohammed
Alshahrani, Haya Mesfer
Kouki, Fadoua
Almalki, Nabil Sharaf
Mahmud, Ahmed
Majdoubi, Jihen
author_sort Alonazi, Mohammed
collection PubMed
description Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.
format Online
Article
Text
id pubmed-10669472
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106694722023-11-19 Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment Alonazi, Mohammed Alshahrani, Haya Mesfer Kouki, Fadoua Almalki, Nabil Sharaf Mahmud, Ahmed Majdoubi, Jihen Biomimetics (Basel) Article Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively. MDPI 2023-11-19 /pmc/articles/PMC10669472/ /pubmed/37999195 http://dx.doi.org/10.3390/biomimetics8070554 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alonazi, Mohammed
Alshahrani, Haya Mesfer
Kouki, Fadoua
Almalki, Nabil Sharaf
Mahmud, Ahmed
Majdoubi, Jihen
Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title_full Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title_fullStr Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title_full_unstemmed Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title_short Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment
title_sort deep convolutional neural network with symbiotic organism search-based human activity recognition for cognitive health assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669472/
https://www.ncbi.nlm.nih.gov/pubmed/37999195
http://dx.doi.org/10.3390/biomimetics8070554
work_keys_str_mv AT alonazimohammed deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment
AT alshahranihayamesfer deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment
AT koukifadoua deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment
AT almalkinabilsharaf deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment
AT mahmudahmed deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment
AT majdoubijihen deepconvolutionalneuralnetworkwithsymbioticorganismsearchbasedhumanactivityrecognitionforcognitivehealthassessment